About: Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts

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About: Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts
Creator	Funk, Sebastian Kucharski, Adam Abbott, Sam Gimma, Amy Hellewell, Joel Bosse, Nikos Eggo, Rosalind Jarvis, Christopher Munday, James Russell, Timothy John Edmunds, W
Source	MedRxiv
abstract	Background: To assess the viability of isolation and contact tracing to control onwards transmission from imported cases of 2019-nCoV. Methods: We developed a stochastic transmission model, parameterised to the 2019-nCoV outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a 2019 nCoV-like pathogen. We considered scenarios that varied in: the number of initial cases; the basic reproduction number R0; the delay from symptom onset to isolation; the probability contacts were traced; the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings: While simulated outbreaks starting with only 5 initial cases, R0 of 1.5 and little transmission before symptom onset could be controlled even with low contact tracing probability, the prospects of controlling an outbreak dramatically dropped with the number of initial cases, with higher R0, and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1.5 were controllable with under 50% of contacts successfully traced. For R0 of 2.5 and 3.5, more than 70% and 90% of contacts respectively had to be traced to control the majority of outbreaks. The delay between symptom onset and isolation played the largest role in determining whether an outbreak was controllable for lower values of R0. For higher values of R0 and a large initial number of cases, contact tracing and isolation was only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation: We found that in most scenarios contact tracing and case isolation alone is unlikely to control a new outbreak of 2019-nCov within three months. The probability of control decreases with longer delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.
has issue date	2020-02-11(xsd:dateTime)
bibo:doi	10.1101/2020.02.08.20021162
has license	medrxiv
sha1sum (hex)	2cc809ed4d5c3640ee2351fdb1876e8ff6c01b51
schema:url	https://doi.org/10.1101/2020.02.08.20021162
resource representing a document's title	Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts
resource representing a document's body	covid:2cc809ed4d5c3640ee2351fdb1876e8ff6c01b51#body_text
is schema:about of	named entity '2019-NCOV' named entity 'cases' named entity 'contacts' named entity 'TITLE' named entity 'CASES' covid:arg/2cc809ed4d5c3640ee2351fdb1876e8ff6c01b51 named entity 'VIABILITY' named entity '2019-NCOV' named entity 'TO CONTROL' named entity 'ISOLATION' named entity 'CASES' named entity 'FEASIBILITY' named entity 'OUTBREAKS' named entity 'CONTACTS' named entity 'ISOLATION' named entity 'IMPORTED' named entity 'CONTACT TRACING' named entity 'TRANSMISSION' named entity 'ASSESS' named entity 'CONTROLLING' named entity '2019-nCoV' named entity 'symptom' named entity '2019-nCoV' named entity 'MERS' named entity 'high levels' named entity 'severe acute respiratory syndrome' named entity 'reproduction number' named entity 'symptom' named entity 'contact tracing' named entity 'contact tracing' named entity 'symptom' named entity 'subclinical' named entity 'pathogen' named entity 'contact tracing' named entity 'Ebola' named entity 'contact tracing' named entity 'probability' named entity 'normal distribution' named entity 'preprint' named entity 'probability' named entity 'branching process' named entity 'symptom' named entity 'disease transmission' named entity 'contact tracing' named entity 'symptom' named entity 'preprint' named entity 'pathogen' named entity 'contact tracing' named entity 'CC-BY-ND' named entity 'route of transmission' named entity 'achieve control' named entity 'contact tracing' named entity 'public health' named entity 'upper bound' named entity 'probability' named entity 'fomites' named entity 'symptom' named entity 'contact tracing' named entity '2019-nCoV' named entity 'preprint' named entity 'asymptomatic' named entity 'contact tracing' named entity 'contact tracing' named entity 'contact tracing' named entity 'contact tracing' named entity 'achieve control' named entity 'serial interval' named entity 'reproduction number' named entity 'basic reproduction number' named entity 'infection' named entity 'contact tracing' named entity 'probability' named entity 'basic reproduction number' named entity 'contact tracing' named entity '2019-nCoV'

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